Abstract
Local Electricity Markets introduces the fundamental characteristics, needs, and constraints shaping the design and implementation of local electricity markets. It addresses current proposed local market models and lessons from their limited practical implementation. The work discusses relevant decision and informatics tools considered important in the implementation of local electricity markets. It also includes a review on management and trading platforms, including commercially available tools. Aspects of local electricity market infrastructure are identified and discussed, including physical and software infrastructure. It discusses the current regulatory frameworks available for local electricity market development internationally. The work concludes with a discussion of barriers and opportunities for local electricity markets in the future. © 2021 Elsevier Inc.

Abstract
This paper examines which business choices are more likely to increase the profitability and distance to distress of banks, and whether changing business model pays off. We find that the profitability and distance to distress increase with the use of customer deposits and equity, and decrease with size; also, the top performers tend to have a high relationship banking orientation and/or operate a retail focused business model. Furthermore, we document that income diversification only bears a positive impact on the distance to distress of banks highly focused on relationship banking, and size only bears a negative effect on the profitability of these banks as well; additionally, only banks with a low relationship banking orientation significantly benefit from customer deposits. With respect to the effects of business model changes, we find that shifts from the retail diversified funding model to either the retail focused or the large diversified models improve profitability in the medium term. Finally, we find evidence that large diversified banks benefited from internal capital markets during the twin financial crisis by tapping into low-cost funding from subsidiaries. Our results are robust to changes to our baseline model that account for endogeneity and persistency issues.

Abstract
The main goal of this systematic review is to synthesize existing evidence on the use of immersive virtual reality (IVR) to train professionals as well as to identify the main gaps and challenges that still remain and need to be addressed by future research. Following a comprehensive search, 66 documents were identified, assessed for relevance, and analysed. The main areas of application of IVR-based training were identified. Moreover, we identified the stimuli provided, the hardware used and information regarding training evaluation. The results showed that the areas in which a greater number of works were published were those related to healthcare and elementary occupations. In hardware, the most commonly used equipment was head mounted displays (HMDs), headphones included in the HMDs and handheld controllers. Moreover, the results indicated that IVR training systems are often evaluated manually, the most common metric being questionnaires applied before and after the experiment, and that IVR training systems have a positive effect in training professionals. We conclude that the literature is insufficient for determining the effect of IVR in the training of professionals. Although some works indicated promising results, there are still relevant themes that must be explored and limitations to overcome before virtual training replaces real-world training.

Abstract
This paper proposes a camera-to-3D Light Detection And Ranging calibration framework through the optimization of atomic transformations. The system is able to simultaneously calibrate multiple cameras with Light Detection And Ranging sensors, solving the problem of Bundle. In comparison with the state-of-the-art, this work presents several novelties: the ability to simultaneously calibrate multiple cameras and LiDARs; the support for multiple sensor modalities; the calibration through the optimization of atomic transformations, without changing the topology of the input transformation tree; and the integration of the calibration framework within the Robot Operating System (ROS) framework. The software pipeline allows the user to interactively position the sensors for providing an initial estimate, to label and collect data, and visualize the calibration procedure. To test this framework, an agricultural robot with a stereo camera and a 3D Light Detection And Ranging sensor was used. Pairwise calibrations and a single calibration of the three sensors were tested and evaluated. Results show that the proposed approach produces accurate calibrations when compared to the state-of-the-art, and is robust to harsh conditions such as inaccurate initial guesses or small amount of data used in calibration. Experiments have shown that our optimization process can handle an angular error of approximately 20 degrees and a translation error of 0.5 meters, for each sensor. Moreover, the proposed approach is able to achieve state-of-the-art results even when calibrating the entire system simultaneously.

Abstract
Purpose The COVID-19 pandemic has significantly impacted the European Union (EU) through heavy pressure on health services, business activity and people's life. To mitigate these effects, government agencies, civil society and the private sector are working together in proposing innovative initiatives. In this sense, this study aims to characterize and explore the relevance of these projects to mitigate the effects of COVID-19. Design/methodology/approach The Observatory of Public Sector Innovation provided by the Organization for Economic Co-operation and Development was considered to enable the identification and exploration of innovative projects to combat COVID-19. A methodology based on mixed methods is adopted to initially identify quantitatively the distribution of these projects, followed by a qualitative approach based on thematic analysis that allows exploring their relevance. Findings A total of 206 initiatives in the EU have been identified. The distribution of these projects is quite asymmetric, with Portugal and Austria totaling 33.52% of these projects. Most of these projects focus on the areas of public health, infection detection and control, virtual education, local commerce, digital services literacy, volunteering and solidarity and hackathons. Originality/value This work is relevant to identifying and understanding the various areas in which COVID-19 initiatives have been developed. This information is of great relevance for the actors involved in this process to be able to replicate these initiatives in their national, regional and local contexts.

Abstract
Deep Learning (DL) training requires efficient access to large collections of data, leading DL frameworks to implement individual I/O optimizations to take full advantage of storage performance. However, these optimizations are intrinsic to each framework, limiting their applicability and portability across DL solutions, while making them inefficient for scenarios where multiple applications compete for shared storage resources. We argue that storage optimizations should be decoupled from DL frameworks and moved to a dedicated storage layer. To achieve this, we propose a new Software-Defined Storage architecture for accelerating DL training performance. The data plane implements self-contained, generally applicable I/O optimizations, while the control plane dynamically adapts them to cope with workload variations and multi-tenant environments. We validate the applicability and portability of our approach by developing and integrating an early prototype with the TensorFlow and PyTorch frameworks. Results show that our I/O optimizations significantly reduce DL training time by up to 54% and 63% for TensorFlow and PyTorch baseline configurations, while providing similar performance benefits to framework-intrinsic I/O mechanisms provided by TensorFlow.